Open Access Method

A distance difference matrix approach to identifying transcription factors that regulate differential gene expression

Pieter De Bleser1,2,3*, Bart Hooghe1,2,3, Dominique Vlieghe1,2,3 and Frans van Roy2,3

1 Bioinformatics Core, VIB, B-9052 Ghent, Belgium

2 Department for Molecular Biomedical Research, VIB, B-9052 Ghent, Belgium

3 Department of Molecular Biology, Ghent University, B-9052 Ghent, Belgium

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Genome Biology 2007, 8:R83 doi:10.1186/gb-2007-8-5-r83

Published: 16 May 2007

Additional files

Additional data file 1:

DDM-MDS plot of an artificial example, demonstrating the power of the approach (Figure S1).

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Open Data

Additional data file 2:

Two background distributions of the distance of a PWM to the origin of the DDM-MDS plot, for two different PWMs, showing the different scales of the distributions (Figures S2 and S3).

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Open Data

Additional data file 3:

DDM-MDS plots showing the minor effects of masking repeat sequences in the promoter sequences on the results for the E2F and p53 datasets (Figures S4 and S5).

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Open Data

Additional data file 4:

DDM-MDS results for the c-Myc dataset (Figure S6, Tables S1 and S2).

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Open Data

Additional data file 5:

Comparison of our DDM-MDS approach with some of the available alternative methods: CLOVER, LogicMotif, POCO, CREME, CMA (Tables S3 and S4).

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Open Data

Additional data file 6:

Comparison of the DDM-MDS plots obtained with the 800 bp and 1,500 bp upstream promoter regions of the E2F (Figure S7) and p53 (Figure S8) datasets.

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Open Data

Additional data file 7:

Calculation of the distance difference matrix (Figure S9).

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Open Data